Reducing the budget to 100 gives the following error instead:
python3.10/site-packages/nevergrad/parametrization/data.py:309: RuntimeWarning: overflow encountered in subtract
return reference._to_reduced_space(self._value - reference._value)
Lastly, using bounds [-np.inf, np.inf] works for some optimizers but not others. For example, NGOpt with a budget of 100 works just fine, but NGOpt with a budget of 1000 returns nan without warning.
Expected Results
I want to be able to set large bounds without weird errors. In particular, I have a large vector of decision variables, and some of them have bounds while others do not. Ideally, I would want to set bounds using set_bounds for the entire vector, where the variables without bounds have some large values or np.inf.
Steps to reproduce
Observed Results
I'm getting the following:
Changing the bounds to something more reasonable like
[-1e10, 1e10]
doesn't produce the first errors but still results in the following:Reducing the budget to
100
gives the following error instead:Lastly, using bounds
[-np.inf, np.inf]
works for some optimizers but not others. For example,NGOpt
with a budget of100
works just fine, butNGOpt
with a budget of1000
returnsnan
without warning.Expected Results
I want to be able to set large bounds without weird errors. In particular, I have a large vector of decision variables, and some of them have bounds while others do not. Ideally, I would want to set bounds using
set_bounds
for the entire vector, where the variables without bounds have some large values ornp.inf
.